The aim of this paper is to present a review of current AI applications in the water domain and to develop some tentative insights on what “responsible AI” could mean there. Under the term “responsible AI”, many initiatives are now being launched to compile guidelines for the principles and values according to which AI is to be developed.
While neither of these extremes seem to be constructive, with the current rise of AI techniques it is important to think about the desired role of AI in society ( IEEE, 2019). Some present dystopian views about autonomous systems “taking control”, while others see AI as a panacea for many of today's societal challenges ( Russell, 2019). With that, AI has also become a topic people have strong opinions about. Crevier, 1993), its current use and impact are unprecedented. Compared to these other fields, the use of AI in the water domain is still relatively modest.Īlthough AI in itself is not new (cf. Notable application domains include transportation (autonomous cars), energy, healthcare, and manufacturing. While digitalization comes in many forms, in recent years artificial intelligence (AI), including machine learning, has gained enormous traction. These insights suggest that the development and application of responsible AI techniques for the water sector should not be left to data scientists alone, but requires concerted effort by water professionals and data scientists working together, complemented with expertise from the social sciences and humanities.ĭigitalization is permeating society in many ways and the role of digital technologies is only expected to increase. We also identify three insights pertaining to the water sector in particular: the use of AI techniques in general, and many-objective optimization in particular, that allow for a pluralism of values and changing values the use of theory-guided data science, which can avoid some of the pitfalls of strictly data-driven models and the ability to build on experiences with participatory decision-making in the water sector. Building on the reviewed literature, four categories of application are identified: modeling, prediction and forecasting, decision support and operational management, and optimization.
This paper presents a review of current AI applications in the water domain and develops some tentative insights as to what “responsible AI” could mean there. Compared to sectors like energy, healthcare, or transportation, the use of AI-based techniques in the water domain is relatively modest. With that have also come initiatives for guidance on how to develop “responsible AI” aligned with human and ethical values. Recent years have seen a rise of techniques based on artificial intelligence (AI).